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Improved Association Rule Modelling Using Various Machine Learning Modules for Large Datasets
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There are four modules namely Modified Apriori Algorithm (MAA), Crumb Based Association Rule Mining (CBARM), Inter-transaction Association Rule (IAR) miner and Categorized and Bounded Inter-Transaction (CBIT) proposed in this research work. The methodology of data mining is a relatively new field of study that has grown over the course of several decades of research and practise, drawing on the findings made in a wide variety of other fields of study. The reality that data mining studies and implementations are exceedingly difficult cannot be avoided in any manner. The development of data mining follows a process that is analogous to the development of any other new technology. This process begins with the presentation of an idea and is then followed by stages in which the concept is accepted, major research and exploration is conducted, incremental application is performed, and finally mass deployment occurs. The great majority of researchers working in the academic world are of the opinion that the process of data mining is still in its infancy in terms of both research and investigation.
Keywords
Association Rule, Machine Learning, Rule Mining
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